Traffic Sign Detection and Recognition using Computer Vision

Traffic Sign Detection and Recognition using Computer Vision

Chandra Shekhar Azad1, Hemant Bisht2, Mohit Aggarwal3, Naman Gupta4, Varun Sharma5

Department of Computer Science and Engineering (Data science)

Inderprastha Engineering College, Ghaziabad, India.

Chandrashekhar.azad@ipec.org.in , bishthemant169@gmail.com

mohitaggarwal775@gmail.com3, namanguptapersonal@gmail.com4,

Varun4229sharma@gmail.com5

ABSTRACT

The rapid evolution of technology is simplifying tasks through automation, with one significant example being Advanced Driver Assistance Systems (ADAS). ADAS encompasses a range of safety features and technologies aimed at assisting drivers and enhancing vehicle safety. These systems, including adaptive cruise control, lane departure warning, automatic emergency braking, and blind-spot monitoring, among others, exemplify the integration of technology into everyday life. The application of ADAS is ‘Traffic Sign Detection and Recognition” (TSDR). TSDR is the system in which traffic signs are automatically detected and recognized. It plays a crucial role for the one who is driving the vehicle. As the driver needs to stay focused on the road while driving, the drivers might miss some of the road signs which can be dangerous for the driver of the vehicle as well as for other drivers. The implementation of the Traffic Sign Detection and Recognition (TSDR) system aims to mitigate risks by employing Computer Vision and machine learning algorithms like Convolutional Neural Networks (CNN). This technology automates the detection and recognition of road signs, minimizing human intervention and the potential for errors. Through this process, the system can accurately identify signs without relying on human interpretation.

Keywords: Computer Vision, Image Processing, CNN, TENSORFLOW, Traffic Sign Detection, Traffic Sign Recognition, Advance Driver Assistance System.